Data Analysis and Knowledge Discovery

by Nikola K. Kasabov, Brendon J. Woodford, Michael J. Watts

This book begins by addressing the fundamental differences between data, information, and knowledge. It then considers how data can be effectively characterized in light of its diverse forms and origins before looking at the various aspects of data acquisition and storage. The authors then deal with several topics related to information theory, including measurement, probability, and entropy, and consider how data is analyzed and transformed into information. Finally, they look to computational modeling for both problem solving and knowledge discovery, employing methods derived from statistics and computer science and discussing selection, use, and particular applications of these methods.

400 pages, Hardcover

First published March 15, 2011